Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach

被引:29
作者
Xu, Ziliang [1 ]
Yang, Xuejuan [1 ]
Gao, Ming [2 ,3 ,4 ]
Liu, Lin [1 ]
Sun, Jinbo [1 ]
Liu, Peng [1 ]
Qin, Wei [1 ]
机构
[1] Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Sch Life Sci & Technol, Xian, Shaanxi, Peoples R China
[2] Northwest Womens & Childrens Hosp, Assisted Reprod Ctr, Xian, Shaanxi, Peoples R China
[3] Fourth Mil Med Univ, Xijing Hosp, Dept Urol, Xian, Shaanxi, Peoples R China
[4] China Acad Chinese Med Sci, Xiyuan Hosp, Dept Androl, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
lifelong premature ejaculation; feature selection; functional magnetic resonance imaging; support vector machine; functional connectivity; SEXUAL MEDICINES GUIDELINES; ELECTROCONVULSIVE-THERAPY; INTERNATIONAL SOCIETY; ACTIVATION; DIAGNOSIS;
D O I
10.3389/fnins.2019.00448
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 +/- 0.14, sensitivity of 0.92 +/- 0.18, specificity of 0.72 +/- 0.30, and recall index of 0.85 +/- 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample t-tests with family-wise error correction were used to compare these features that occur more than 500 times during training steps between LPE patients and HCs. Four FCs, (1) between left medial part of orbital frontal cortex (mOFC) and right mOFC, (2) between the left rectus and right postcentral gyrus, (3) between the right insula and left pallidum, and (4) between the right middle part of temporal pole and right inferior part of temporal gyrus showed significant group difference. These results demonstrate that resting-state brain FC might be a discriminating feature to distinguish LPE patients from HCs. These classification features, especially the FC between bilateral mOFC, provide underlying abnormal central functional targets in LPE etiology, which offers a novel alternative target for future intervention in LPE treatment.
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页数:7
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